Improving drone localisation around wind turbines using monocular model-based tracking

Oliver Moolan-Feroze, Konstantinos Karachalios, Dimitrios Nikolaidis, Andrew Calway

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

4 Citations (Scopus)
168 Downloads (Pure)

Abstract

We present a novel method of integrating image-based measurements into a drone navigation system for the automated inspection of wind turbines. We take a model-based tracking approach, where a 3D skeleton representation of the turbine is matched to the image data. Matching is based on comparing the projection of the representation to that inferred from images using a convolutional neural network. This enables us to find image correspondences using a generic turbine model that can be applied to a wide range of turbine shapes and sizes. To estimate 3D pose of the drone, we fuse the network output with GPS and IMU measurements using a pose graph optimiser. Results illustrate that the use of the image measurements significantly improves the accuracy of the localisation over that obtained using GPS and IMU alone.
Original languageEnglish
Title of host publication2019 IEEE International Conference on Robotics and Automation (ICRA 2019)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages7713 - 7719
Number of pages7
ISBN (Print)9781538681763
DOIs
Publication statusE-pub ahead of print - 12 Aug 2019
Event2019 IEEE International Conference on Robotics and Automation (ICRA 2019) - Montreal, Canada
Duration: 20 May 201924 May 2019
https://www.icra2019.org/

Publication series

Namel: IEEE International Conference on Robotics and Automation
PublisherIEEE
ISSN (Print)1050-4729
ISSN (Electronic)2577-087X

Conference

Conference2019 IEEE International Conference on Robotics and Automation (ICRA 2019)
Abbreviated titleICRA2019
CountryCanada
CityMontreal
Period20/05/1924/05/19
Internet address

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